Application of deep convolutional neural networks systems in autonomous vehicles

The currently available sensor on those self-driving automobiles does a poor job of detecting the state of the road ahead of them. However, daytime and nighttime weather-related road conditions require safe driving. Deep learning study for daytime roadway identification uses data from a vehicle sensor. An overview of the use of deep convolutional neural networks (CNNs) in autonomous cars is given in this paper. The paper starts by going through the difficulties of creating autonomous vehicles and how CNNs can be utilised to overcome these difficulties. The author thoroughly explains the basis of CNN and how it may be used for tasks like object detection, lane finding, and recognition of traffic signals. The research also examines how CNN focus techniques and transfer learning can be applied to autonomous vehicles. The authors conclude by highlighting the limits of current CNNs in this field and suggesting future research. This review paper gives academic scholars and industry experts a current overview of CNNs in cars.

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